AI Scientist - Machine Learning (KR/US)

Responsibilities

• Design and implement Transformer-based architectures for time-series prediction and sequence modeling, across both univariate and multivariate data.
• Drive the full machine learning lifecycle—from exploratory data analysis to model deployment, monitoring, and continuous improvement.
• Conduct rigorous benchmarking, ablation studies, and performance optimization to ensure robustness and efficiency.
• Collaborate closely with data scientists, engineers, and product managers to translate complex business requirements into scalable technical solutions.
• Partner with software engineers to scale and productize ML algorithms within manufacturing AI software products.
• Contribute to Gauss Labs’ intellectual property portfolio through patents and high-impact technical publications.
• Mentor junior team members and play an active role in shaping the team’s AI roadmap and long-term strategy.

Key Qualifications

• Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
• 3+ years of hands-on experience in deep learning, with a strong focus on sequence modeling and time-series forecasting.
• In-depth expertise in Transformer architectures and their applications beyond natural language processing.
• Proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Solid mathematical foundation in statistics, optimization, and signal processing.
• Familiarity with hybrid modeling approaches that combine deep learning and traditional statistical methods.
• Experience working with noisy, sparse, or irregularly sampled time-series data.
• Strong publication track record in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR).
• Practical experience deploying ML models in production environments, with knowledge of MLOps best practices.
• [Nice to have] Familiar with state-of-the-art neural networks architecture. Preferably had experience in innovation in new architecture such as transformer based models.